COLMAR：用于多智能体主动3D重建的协作视图策略学习
COLMAR: Cooperative View Policy Learning for Multi-Agent Active 3D Reconstruction
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中文总结 AI 辅助
研究多智能体主动3D重建中协调效率低的问题，提出COLMAR框架，将视点分配作为共享策略优化，用参数共享PPO训练，结合3DGS重建，实验表明相比基线在传感预算匹配时重建精度和覆盖率显著提升。
中文摘要 AI 辅助
主动3D重建需要在有限的传感预算下选择信息丰富的视点。在多智能体环境中，诸如冗余观测和空间聚类等协调效率低下的问题会显著降低重建质量。我们提出了COLMAR，一种用于多智能体主动3D重建的协作视图策略学习框架。COLMAR将视点分配制定为基于地图中心观测的共享策略优化，并引入了一个重建感知目标，以促进重叠感知覆盖、团队级发现和碰撞安全探索。从增量重建更新中获得的密集反馈使探索行为与下游几何质量保持一致。该策略使用参数共享近端策略优化（PPO）进行训练，在部署时每个智能体独立选择动作，以融合的团队地图为条件，决策时无需智能体间消息传递。然后使用3D高斯溅射（3DGS）对选定的视点进行重建，以进行高保真光度评估。在GLEAM和Replica上的实验表明，与启发式和非协作基线相比有持续改进，在匹配的传感预算下，重建精度提高了54%，覆盖率提高了49%。
英文摘要
Active 3D reconstruction requires selecting informative viewpoints under limited sensing budgets. In multi-agent settings, coordination inefficiencies such as redundant observations and spatial clustering can significantly reduce reconstruction quality. We present COLMAR, a cooperative view policy learning framework for multi-agent active 3D reconstruction. COLMAR formulates viewpoint allocation as a shared policy optimization over map-centric observations and introduces a reconstruction-aware objective that promotes overlap-aware coverage, team-level discovery, and collision-safe exploration. Dense feedback derived from incremental reconstruction updates aligns exploration behavior with downstream geometric quality. The policy is trained using parameter-sharing Proximal Policy Optimization (PPO) with independent per-agent action selection at deployment, conditioned on a fused team map and without inter-agent message passing for decision making. Selected viewpoints are then reconstructed with 3D Gaussian Splatting (3DGS) for high-fidelity photometric evaluation. Experiments on GLEAM and Replica demonstrate consistent improvements over heuristic and non-cooperative baselines, achieving up to 54% higher reconstruction accuracy and 49% greater coverage under matched sensing budgets.
发表机构
- Purdue University（普渡大学）
- DEVCOM Army Research Laboratory（陆军研究实验室）
机构由 AI 辅助整理，请以论文原文为准。
